Mutational Signatures SigneR Analysis
Garrett Kinnebrew
2021-February-24
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(cache = TRUE)
# Git setup / verification
git_tag <- stringr::str_sub(git2r::revparse_single(git2r::repository(here::here()),"HEAD")$sha, 1, 8)
git_status <- git2r::status(untracked = FALSE)
if(!interactive()){
if(length(git_status$staged) != 0 ||
length(git_status$unstaged) != 0){
stop("There are uncommited changes to the repository")
}
}
# Load Libraries
suppressPackageStartupMessages({
library(tidyverse)
library(glue)
library(stringr)
library(signeR)
library(SomaticSignatures)
library(plotly)
library(cowplot)
})
# Utility Functions
theme_modify <- function(plot, ..., default_theme = theme_get()){
update_functions <- list(...)
theme_values <- list()
for(theme_var in names(update_functions)){
theme_var_value <- plot$theme[[theme_var]]
if(is.null(theme_var_value)){
theme_var_value <- default_theme[[theme_var]]
}
theme_var_value <- update_functions[[theme_var]](theme_var_value)
theme_values[[theme_var]] <- theme_var_value
}
do.call(theme, theme_values)
}1 Load Data
1.1 Load and clean sample information
We’re loading the sample description matrix from the provided excel spreadsheat. We parse the sample names for a short sample_id and the Genotype field for two variables: Tsg (true or false) and Fhit (+/+ or -/-)
sample_info <-
readxl::read_excel("Data/Sample information.xlsx") %>%
filter(!is.na(Genotype)) %>%
mutate(Tsg = str_detect(Genotype, "Tsg")) %>%
mutate(Fhit = case_when(
str_detect(Genotype, stringr::fixed("Fhit +/+")) ~ "+/+",
str_detect(Genotype, stringr::fixed("Fhit -/-")) ~ "-/-"
)) %>%
mutate(sample_id = str_replace_all(`Abreviated Sample name`, " ", ""))sample_info| Abreviated Sample name | Genotype | Tissue | seq library type | Tsg | Fhit | sample_id |
|---|---|---|---|---|---|---|
| CT2-1F | Fhit -/- | liver | non-PCR amplified | FALSE | -/- | CT2-1F |
| CT2-3F | Tsg;Fhit -/- | liver | non-PCR amplified | TRUE | -/- | CT2-3F |
| CT3-1M | Fhit -/- | liver | non-PCR amplified | FALSE | -/- | CT3-1M |
| CT3- 3M | Tsg;Fhit +/+ | liver | non-PCR amplified | TRUE | +/+ | CT3-3M |
| CT4-2F | Tsg;Fhit -/- | liver | non-PCR amplified | TRUE | -/- | CT4-2F |
| CT4-3F | Fhit +/+ | liver | non-PCR amplified | FALSE | +/+ | CT4-3F |
| CT9-1F | Tsg;Fhit +/+ | liver | non-PCR amplified | TRUE | +/+ | CT9-1F |
| CT11-2F | Fhit +/+ | liver | non-PCR amplified | FALSE | +/+ | CT11-2F |
| CT15-1M | Fhit +/+ | liver | non-PCR amplified | FALSE | +/+ | CT15-1M |
| CT17-1M | Tsg;Fhit -/- | liver | non-PCR amplified | TRUE | -/- | CT17-1M |
| CT18-2F | Fhit -/- | liver | non-PCR amplified | FALSE | -/- | CT18-2F |
| CT20-2M | Fhit -/- | liver | non-PCR amplified | FALSE | -/- | CT20-2M |
| CT22-3M | Tsg;Fhit +/+ | liver | non-PCR amplified | TRUE | +/+ | CT22-3M |
| CT23-2F | Tsg;Fhit +/+ | liver | non-PCR amplified | TRUE | +/+ | CT23-2F |
| CT24-1M | Tsg;Fhit -/- | liver | non-PCR amplified | TRUE | -/- | CT24-1M |
| K116 | Fhit +/+ | sperm | PCR amplified | FALSE | +/+ | K116 |
| K124 | Fhit -/- | sperm | PCR amplified | FALSE | -/- | K124 |
| KB15 | Fhit -/- | sperm | PCR amplified | FALSE | -/- | KB15 |
1.2 Load reference assembly
The complete reference assembly for these files was downloaded from https://www.ncbi.nlm.nih.gov/assembly/GCF_000001635.26/ as a “Genomic FASTA (.fna)” file.
reference_assembly <- FaFile("Data/GCF_000001635.26_GRCm38.p6_genomic.fna")1.3 Load SNV data
As a preliminary analysis I’m loading the liver sample VCF files. These are matched to the sample metadata based on the short sample ids (e.g. CT4-2F) after fixing one inconsistency in naming (“CT23_2F” vs “CT23-2F”).
#VRanges object containing data from vcf files
vr <-
fs::dir_ls("Data", glob = "*.vcf.gz") %>% # list vcf files
# purrr::keep(str_detect, "liver") %>% # keep only the liver samples
str_replace("KB15Fhit_1.filter.snp.vcf.gz$",
"KB15Fhit_1.filter.snp.vcf") %>% # this file errors on gzipped version
map(~{message(.x); readVcfAsVRanges(.x)}) %>% # read vcf files into VRanges objects
{do.call(c, unname(.))} # merge VRanges objects## Data/CT11-2F_Fhit_liverA_1.filter.snp.vcf.gz## Data/CT15-1M_Fhit_liverA_1.filter.snp.vcf.gz## Data/CT17-1M_Tsg_Fhit_liverA_1.filter.snp.vcf.gz## Data/CT18-2F_Fhit_liverA_1.filter.snp.vcf.gz## Data/CT2-1F_Fhit_liverA_1.filter.snp.vcf.gz## Data/CT2-3F_Tsg_Fhit_liverA_1.filter.snp.vcf.gz## Data/CT20-2M_Fhit_liverA_1.filter.snp.vcf.gz## Data/CT22-3M_Tsg_Fhit_liverA_1.filter.snp.vcf.gz## Data/CT23_2F_Tsg_Fhit_liverA_1.filter.snp.vcf.gz## Data/CT24-1M_Tsg_Fhit_liverA_1.filter.snp.vcf.gz## Data/CT3-1M_Fhit_liverA_1.filter.snp.vcf.gz## Data/CT3-3M_Tsg_Fhit_liverA_1.filter.snp.vcf.gz## Data/CT4-2F_Tsg_Fhit_liverA_1.filter.snp.vcf.gz## Data/CT4-3F_Fhit_liverA_1.filter.snp.vcf.gz## Data/CT9-1F_Tsg_Fhit_liverA_1.filter.snp.vcf.gz## Data/K116Fhit_1.filter.snp.vcf.gz## Data/K124Fhit_1.filter.snp.vcf.gz## Data/KB15Fhit_1.filter.snp.vcfWe keep only those SNVs for which all filters passed. This includes 2 filters: “Low quality” and “QD < 2.0 || FS > 60.0 || MQ <40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0”
PASS <-
softFilterMatrix(vr) %>%
apply(all, MARGIN = 1)
vr <- vr[PASS]
message(glue("{sum(PASS)} of {length(PASS)} ({sprintf('%.2f', 100 * sum(PASS)/length(PASS))}%) SNVs across all files passed filters."))## 1063834 of 1813765 (58.65%) SNVs across all files passed filters.#Sample info replicated to match the rows in `vr`
vr_metadata <-
vr %>%
sampleNames() %>% as.vector() %>% # extract sampleNames
str_replace(stringr::fixed("CT23_2F"), "CT23-2F") %>% # fix inconsistently named vcf file name
str_extract("^[^_]+") %>% # extract sample_id from names
str_replace("Fhit$", "") %>% # sperm samples have 'Fhit' appended in file names
{tibble(sample_id = .)} %>%
left_join(sample_info, by = "sample_id") # combine with sample info
#add metadata columns to `vr`
vr$sample_id <- vr_metadata$sample_id
vr$Tissue <- vr_metadata$Tissue
vr$Genotype <- vr_metadata$Genotype
vr$Fhit <- vr_metadata$Fhit
vr$Tsg <- vr_metadata$Tsg
#replace sample names with `sample_id`
sampleNames(vr) <- vr$sample_id1.4 Extract Mutation Contexts
The mutation contexts are extracted using the SomaticSignatures::mutationContext function and supplying it with the reference assembly loaded above. This assembly has chromosomes split in smaller contigs which match the contig identifiers in the VCF files. There is no need to translate to chromosome numbers.
#add mutation context information to `vr`
vr <- mutationContext(vr, reference_assembly)1.5 Analyze and Filter Common SNVs
# Count number of samples each location + alteration combination appears in
snvs <-
vr %>%
as_tibble() %>%
mutate(length = abs(end - start) + 1) %>%
{assertthat::assert_that(all(.$length == 1)); .} %>%
group_by(seqnames, start, alteration, context) %>%
select(sample_id, Tissue, Genotype) %>%
tidyr::nest() %>%
mutate(count = data %>% map_int(nrow),
count_liver = data %>% map_int(. %>% filter(Tissue == "liver") %>% nrow)) %>%
ungroup()## Adding missing grouping variables: `seqnames`, `start`, `alteration`, `context`snvs %>%
{
ggplot(., aes(x = count, group = count)) +
geom_histogram(binwidth = 1) +
ggtitle("Number of Samples Each SNV Occurs in")
} %>% ggplotly()## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.snvs %>%
mutate(count_sperm = count - count_liver) %>%
group_by(count_liver, count_sperm) %>%
summarise(n_snvs = n(), .groups = "drop") %>%
{
ggplot(., aes(x = count_liver, y = count_sperm, fill = n_snvs)) +
geom_tile() +
scale_fill_viridis_c(option = "B") +
labs(title = "Overlap between common SNVs in Liver and Sperm",
x = "Liver Samples Containing SNV",
y = "Sperm Samples Containing SNV",
fill = "Unique SNVs")
} %>%
ggplotly()Most SNVs were unique to one sample or present in both sperm and liver samples.
n_samples <- nrow(sample_info)
common_snvs <-
snvs %>%
filter(count > .5 * n_samples) %>%
select(seqnames, start, alteration) %>%
mutate(snv_id = glue("{seqnames}_{start}_{alteration}"))
message(glue("{nrow(common_snvs)} of {nrow(snvs)} ({sprintf('%.2f', 100 * nrow(common_snvs) / nrow(snvs))}%) SNVs classified as 'common' due to occurance in >50% of samples."))## 50284 of 156235 (32.18%) SNVs classified as 'common' due to occurance in >50% of samples.vr$snv_id <-
vr %>%
as_tibble() %>%
with(glue("{seqnames}_{start}_{alteration}"))
COMMON_SNV <- vr$snv_id %in% common_snvs$snv_id
vr <- vr[!COMMON_SNV]
message(glue("{sum(COMMON_SNV)} of {length(COMMON_SNV)} ({sprintf('%.2f', 100 * sum(COMMON_SNV) / length(COMMON_SNV))}%) SNV calls removed as 'common'."))## 793511 of 1063834 (74.59%) SNV calls removed as 'common'.snv_calls <-
snvs %>%
# create `motif` column in the format signeR uses
tidyr::extract(col = "alteration",
regex = "([TCGA])([TCGA])",
into = c(".from", ".to"),
remove = FALSE) %>%
tidyr::extract(col = "context",
regex = "([TCGAN])\\.([TCGAN])",
into = c(".l_context", ".r_context"),
remove = FALSE) %>%
mutate(motif = glue("{.from}>{.to}:{.l_context}{.from}{.r_context}")) %>%
select(-starts_with('.')) %>%
mutate(snv_id = glue("{seqnames}_{start}_{alteration}")) %>%
mutate(is_common = snv_id %in% common_snvs$snv_id) %>%
tidyr::unnest(data)snv_calls %>%
filter(!str_detect(context, "N")) %>%
group_by(alteration, context, is_common) %>%
summarise(n = n(), .groups = "drop") %>%
mutate(is_common = case_when(
is_common ~ "Common",
!is_common ~ "Not Common"
)) %>%
mutate(alteration = str_replace(alteration, "^(.)(.)$", "\\1>\\2")) %>%
{
ggplot(., aes(x = context, y = n)) +
geom_bar(stat = "identity") +
facet_grid(is_common ~ alteration) +
theme(axis.text.x = element_text(angle = 90)) +
labs(title = "Motif Frequencies in Common and Non Common SNVs")
} %>%
ggplotly()1.5.1 Principle Component Analysis
pca_result <-
snv_calls %>%
filter(!str_detect(context, "N")) %>%
mutate(group_id =
case_when(
is_common ~ "Common SNVs",
TRUE ~ sample_id
)) %>%
group_by(group_id, motif) %>%
summarise(n = n(), .groups = "drop") %>%
#normalize by sample
group_by(group_id) %>%
mutate(n = n / sum(n)) %>%
ungroup() %>%
pivot_wider(names_from = group_id, values_from = n) %>%
column_to_rownames("motif") %>%
as.matrix() %>% t() %>%
pcaMethods::pca(nPcs = 2, center = TRUE, scale = "vector")
pca_result@scores %>%
as.data.frame() %>%
rownames_to_column("sample_id") %>%
left_join(sample_info, by = "sample_id") %>%
mutate(group = case_when(
str_detect(sample_id, "Common") ~ "Common SNVs",
TRUE ~ Tissue
)) %>%
{
ggplot(., aes(x = PC1, y = PC2, color = group)) +
geom_point(aes(text = glue("sample_id = {sample_id}",
"genotype = {Genotype}",
.sep = "\n")))
} %>%
ggplotly()## Warning: Ignoring unknown aesthetics: textThis plot shows PCA analysis of filtered sample motif frequencies compared to the a single pseudo-sample comprising the all of the SNV calls for “common” SNVs combined.
pca_result@loadings %>%
as.data.frame() %>%
rownames_to_column("motif") %>%
tidyr::extract(col = "motif",
regex = "([TCGA]>[TCGA]):([TCGA][TCGA][TCGA])",
into = c("alteration", "context"),
remove = FALSE) %>%
{
ggplot(., aes(x = PC1, y = PC2, color = alteration)) +
geom_point(aes(text = glue("motif = {motif}", .sep = "\n"))) +
labs(title = "Loadings Plot of PCA results")
} %>%
ggplotly()## Warning: Ignoring unknown aesthetics: textNo clear pattern is seen in which motifs distinguish the groups.
1.6 Mutation Frequency Matrix
# Extract motif *count* matrix
motif_counts <-
tibble(sample_id = vr$sample_id,
alteration = as.character(vr$alteration),
context = as.character(vr$context)) %>%
# create `motif` column in the format signeR uses
tidyr::extract(col = "alteration",
regex = "([TCGA])([TCGA])",
into = c(".from", ".to"),
remove = FALSE) %>%
tidyr::extract(col = "context",
regex = "([TCGA])\\.([TCGA])",
into = c(".l_context", ".r_context"),
remove = FALSE) %>%
mutate(motif = glue("{.from}>{.to}:{.l_context}{.from}{.r_context}")) %>%
# count motif occurrences by sample
group_by(sample_id, motif, alteration, context) %>%
summarise(N = n(), .groups = "drop")
# convert to matrix
motif_count_matrix <-
motif_counts %>%
pivot_wider(id_cols = motif, names_from = sample_id, values_from = N) %>%
column_to_rownames("motif") %>%
as.matrix()The motif matrix is then extracted for further analysis. This matrix containing frequencies of each mutation motif for each sample is provided as a *.csv file accompanying this report.
#matrix of sample x motif frequencies
motif_count_matrix %>%
as.data.frame() %>%
rownames_to_column("motif") %>%
select(motif, everything()) %>%
write_csv("./Results/motif_count_matrix.csv")motif_count_matrix %>%
format(digits = 3) %>%
as.data.frame()| CT11-2F | CT15-1M | CT17-1M | CT18-2F | CT2-1F | CT2-3F | CT20-2M | CT22-3M | CT23-2F | CT24-1M | CT3-1M | CT3-3M | CT4-2F | CT4-3F | CT9-1F | K116 | K124 | KB15 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| C>A:ACA | 209 | 197 | 141 | 156 | 177 | 163 | 193 | 180 | 197 | 167 | 188 | 211 | 151 | 191 | 211 | 171 | 178 | 168 |
| C>A:ACC | 116 | 109 | 118 | 112 | 115 | 107 | 126 | 103 | 117 | 118 | 118 | 127 | 94 | 117 | 116 | 158 | 131 | 147 |
| C>A:ACG | 49 | 54 | 39 | 48 | 32 | 43 | 52 | 53 | 51 | 35 | 40 | 52 | 31 | 41 | 51 | 44 | 54 | 55 |
| C>A:ACT | 117 | 96 | 94 | 87 | 102 | 117 | 106 | 99 | 103 | 86 | 89 | 99 | 95 | 88 | 103 | 94 | 94 | 98 |
| C>A:CCA | 140 | 119 | 121 | 98 | 119 | 124 | 130 | 126 | 145 | 112 | 119 | 145 | 97 | 128 | 137 | 115 | 116 | 128 |
| C>A:CCC | 161 | 166 | 149 | 159 | 141 | 155 | 154 | 168 | 161 | 160 | 151 | 177 | 169 | 175 | 194 | 137 | 176 | 148 |
| C>A:CCG | 38 | 39 | 29 | 32 | 35 | 35 | 38 | 44 | 31 | 37 | 35 | 35 | 31 | 31 | 39 | 34 | 30 | 28 |
| C>A:CCT | 113 | 106 | 79 | 89 | 88 | 111 | 105 | 104 | 115 | 95 | 93 | 107 | 80 | 94 | 109 | 111 | 116 | 99 |
| C>A:GCA | 80 | 79 | 85 | 68 | 77 | 68 | 85 | 79 | 82 | 74 | 65 | 87 | 65 | 76 | 90 | 84 | 84 | 94 |
| C>A:GCC | 59 | 55 | 43 | 30 | 44 | 48 | 49 | 47 | 54 | 52 | 52 | 58 | 39 | 48 | 47 | 46 | 55 | 54 |
| C>A:GCG | 33 | 26 | 39 | 26 | 24 | 34 | 28 | 42 | 34 | 34 | 37 | 43 | 27 | 40 | 49 | 32 | 34 | 29 |
| C>A:GCT | 94 | 98 | 78 | 75 | 65 | 75 | 75 | 86 | 93 | 76 | 77 | 99 | 72 | 83 | 99 | 67 | 66 | 68 |
| C>A:TCA | 96 | 79 | 65 | 73 | 72 | 75 | 74 | 86 | 88 | 70 | 60 | 96 | 89 | 83 | 101 | 74 | 77 | 60 |
| C>A:TCC | 108 | 94 | 63 | 74 | 85 | 91 | 76 | 90 | 91 | 77 | 82 | 92 | 69 | 88 | 110 | 103 | 114 | 112 |
| C>A:TCG | 26 | 22 | 23 | 22 | 24 | 21 | 29 | 22 | 23 | 25 | 20 | 26 | 18 | 19 | 24 | 29 | 15 | 22 |
| C>A:TCT | 332 | 329 | 323 | 284 | 267 | 302 | 343 | 324 | 317 | 263 | 331 | 386 | 290 | 307 | 410 | 175 | 154 | 168 |
| C>G:ACA | 219 | 216 | 187 | 179 | 176 | 227 | 219 | 195 | 208 | 217 | 196 | 220 | 180 | 218 | 226 | 229 | 180 | 195 |
| C>G:ACC | 62 | 56 | 54 | 54 | 66 | 63 | 74 | 77 | 52 | 46 | 51 | 66 | 51 | 60 | 73 | 60 | 54 | 52 |
| C>G:ACG | 54 | 57 | 57 | 48 | 63 | 39 | 49 | 49 | 64 | 58 | 46 | 48 | 55 | 56 | 52 | 85 | 82 | 73 |
| C>G:ACT | 119 | 106 | 79 | 87 | 91 | 89 | 103 | 120 | 102 | 77 | 85 | 94 | 62 | 90 | 110 | 91 | 82 | 94 |
| C>G:CCA | 50 | 54 | 43 | 37 | 49 | 43 | 55 | 50 | 47 | 48 | 49 | 54 | 34 | 48 | 46 | 55 | 39 | 46 |
| C>G:CCC | 55 | 39 | 53 | 50 | 47 | 50 | 47 | 45 | 56 | 38 | 46 | 56 | 38 | 42 | 46 | 48 | 60 | 53 |
| C>G:CCG | 38 | 29 | 31 | 28 | 31 | 30 | 23 | 28 | 24 | 30 | 36 | 36 | 25 | 27 | 27 | 52 | 33 | 43 |
| C>G:CCT | 51 | 57 | 42 | 51 | 48 | 50 | 55 | 58 | 54 | 44 | 63 | 64 | 48 | 49 | 56 | 55 | 53 | 58 |
| C>G:GCA | 117 | 106 | 86 | 107 | 68 | 66 | 97 | 133 | 105 | 86 | 113 | 118 | 137 | 112 | 109 | 119 | 139 | 148 |
| C>G:GCC | 74 | 105 | 62 | 91 | 57 | 61 | 52 | 82 | 62 | 72 | 78 | 68 | 93 | 76 | 70 | 68 | 50 | 83 |
| C>G:GCG | 217 | 244 | 183 | 277 | 157 | 165 | 187 | 206 | 182 | 136 | 238 | 258 | 345 | 287 | 196 | 794 | 843 | 805 |
| C>G:GCT | 137 | 128 | 93 | 133 | 107 | 87 | 106 | 105 | 106 | 111 | 134 | 134 | 158 | 131 | 124 | 94 | 89 | 116 |
| C>G:TCA | 52 | 60 | 47 | 52 | 47 | 53 | 52 | 63 | 56 | 46 | 56 | 64 | 49 | 72 | 66 | 74 | 62 | 69 |
| C>G:TCC | 71 | 70 | 55 | 61 | 53 | 66 | 62 | 76 | 65 | 53 | 78 | 78 | 66 | 70 | 81 | 106 | 83 | 87 |
| C>G:TCG | 41 | 42 | 37 | 44 | 29 | 36 | 40 | 42 | 32 | 45 | 44 | 40 | 37 | 34 | 40 | 53 | 43 | 55 |
| C>G:TCT | 222 | 216 | 195 | 162 | 196 | 206 | 211 | 228 | 205 | 177 | 208 | 215 | 170 | 217 | 217 | 155 | 159 | 160 |
| C>T:ACA | 487 | 445 | 393 | 398 | 392 | 445 | 448 | 420 | 452 | 354 | 446 | 463 | 379 | 410 | 451 | 407 | 389 | 449 |
| C>T:ACC | 157 | 167 | 163 | 145 | 146 | 146 | 160 | 174 | 171 | 155 | 159 | 173 | 164 | 150 | 168 | 160 | 145 | 176 |
| C>T:ACG | 200 | 203 | 154 | 164 | 195 | 181 | 204 | 193 | 191 | 135 | 165 | 187 | 161 | 174 | 221 | 164 | 155 | 173 |
| C>T:ACT | 229 | 251 | 192 | 183 | 189 | 199 | 207 | 228 | 197 | 180 | 183 | 246 | 171 | 217 | 220 | 209 | 223 | 223 |
| C>T:CCA | 225 | 236 | 181 | 170 | 187 | 216 | 229 | 239 | 206 | 183 | 202 | 230 | 187 | 206 | 230 | 210 | 186 | 202 |
| C>T:CCC | 319 | 343 | 307 | 312 | 245 | 310 | 333 | 306 | 296 | 279 | 291 | 394 | 293 | 312 | 326 | 369 | 380 | 492 |
| C>T:CCG | 193 | 186 | 146 | 150 | 182 | 171 | 186 | 174 | 168 | 144 | 144 | 190 | 130 | 151 | 172 | 149 | 170 | 144 |
| C>T:CCT | 305 | 338 | 282 | 284 | 266 | 265 | 334 | 340 | 297 | 293 | 312 | 368 | 283 | 284 | 353 | 324 | 374 | 413 |
| C>T:GCA | 160 | 145 | 159 | 140 | 147 | 154 | 153 | 183 | 173 | 134 | 126 | 165 | 133 | 127 | 166 | 134 | 138 | 135 |
| C>T:GCC | 126 | 124 | 109 | 114 | 112 | 115 | 129 | 133 | 122 | 126 | 131 | 130 | 102 | 113 | 135 | 133 | 119 | 133 |
| C>T:GCG | 177 | 164 | 118 | 153 | 151 | 163 | 177 | 172 | 168 | 147 | 159 | 157 | 116 | 162 | 177 | 149 | 133 | 151 |
| C>T:GCT | 165 | 168 | 139 | 149 | 149 | 146 | 159 | 171 | 166 | 143 | 158 | 190 | 146 | 147 | 177 | 164 | 175 | 152 |
| C>T:TCA | 208 | 228 | 163 | 148 | 181 | 174 | 191 | 217 | 218 | 182 | 177 | 241 | 164 | 182 | 222 | 190 | 190 | 185 |
| C>T:TCC | 418 | 419 | 296 | 340 | 333 | 339 | 414 | 371 | 371 | 306 | 380 | 430 | 340 | 370 | 415 | 458 | 395 | 456 |
| C>T:TCG | 132 | 153 | 113 | 128 | 125 | 127 | 137 | 142 | 156 | 116 | 115 | 145 | 113 | 121 | 155 | 108 | 105 | 95 |
| C>T:TCT | 312 | 318 | 264 | 247 | 245 | 272 | 284 | 311 | 285 | 261 | 249 | 338 | 237 | 296 | 313 | 304 | 262 | 307 |
| T>A:ATA | 138 | 136 | 115 | 115 | 137 | 117 | 125 | 133 | 131 | 112 | 116 | 168 | 97 | 129 | 145 | 134 | 113 | 113 |
| T>A:ATC | 74 | 75 | 56 | 73 | 64 | 61 | 70 | 86 | 79 | 58 | 60 | 82 | 73 | 67 | 72 | 66 | 81 | 67 |
| T>A:ATG | 123 | 108 | 72 | 87 | 85 | 82 | 101 | 116 | 109 | 87 | 89 | 127 | 76 | 97 | 108 | 101 | 82 | 96 |
| T>A:ATT | 129 | 155 | 110 | 94 | 111 | 114 | 111 | 138 | 134 | 122 | 116 | 145 | 110 | 108 | 135 | 117 | 97 | 106 |
| T>A:CTA | 71 | 78 | 53 | 48 | 63 | 52 | 76 | 84 | 67 | 59 | 59 | 69 | 52 | 54 | 66 | 41 | 59 | 58 |
| T>A:CTC | 153 | 157 | 127 | 149 | 130 | 154 | 148 | 133 | 153 | 131 | 133 | 161 | 138 | 139 | 147 | 123 | 130 | 116 |
| T>A:CTG | 93 | 101 | 89 | 79 | 87 | 95 | 88 | 92 | 98 | 79 | 81 | 101 | 87 | 79 | 107 | 92 | 89 | 92 |
| T>A:CTT | 100 | 110 | 84 | 83 | 71 | 81 | 88 | 103 | 86 | 101 | 73 | 107 | 82 | 86 | 124 | 83 | 83 | 97 |
| T>A:GTA | 67 | 85 | 48 | 59 | 53 | 49 | 61 | 72 | 71 | 57 | 49 | 86 | 49 | 67 | 75 | 54 | 58 | 70 |
| T>A:GTC | 43 | 45 | 37 | 39 | 49 | 51 | 42 | 46 | 40 | 38 | 44 | 58 | 34 | 42 | 42 | 50 | 45 | 53 |
| T>A:GTG | 173 | 153 | 148 | 149 | 139 | 155 | 154 | 173 | 167 | 141 | 155 | 181 | 155 | 159 | 185 | 144 | 131 | 145 |
| T>A:GTT | 82 | 72 | 64 | 71 | 69 | 80 | 84 | 80 | 76 | 62 | 72 | 98 | 69 | 69 | 91 | 67 | 68 | 71 |
| T>A:TTA | 113 | 131 | 118 | 86 | 106 | 91 | 123 | 124 | 121 | 95 | 96 | 114 | 90 | 79 | 106 | 104 | 106 | 103 |
| T>A:TTC | 59 | 67 | 61 | 63 | 57 | 74 | 65 | 71 | 67 | 55 | 59 | 75 | 51 | 67 | 66 | 67 | 65 | 63 |
| T>A:TTG | 56 | 64 | 63 | 59 | 64 | 47 | 70 | 89 | 65 | 64 | 54 | 63 | 62 | 53 | 82 | 62 | 57 | 64 |
| T>A:TTT | 152 | 147 | 136 | 127 | 118 | 141 | 147 | 176 | 156 | 125 | 139 | 174 | 116 | 151 | 165 | 122 | 113 | 120 |
| T>C:ATA | 367 | 341 | 301 | 280 | 284 | 318 | 292 | 351 | 322 | 277 | 296 | 339 | 339 | 315 | 346 | 313 | 266 | 284 |
| T>C:ATC | 179 | 163 | 174 | 164 | 127 | 133 | 171 | 175 | 160 | 164 | 154 | 206 | 175 | 177 | 190 | 195 | 194 | 228 |
| T>C:ATG | 202 | 196 | 178 | 165 | 154 | 169 | 173 | 208 | 218 | 168 | 151 | 220 | 147 | 186 | 213 | 156 | 153 | 150 |
| T>C:ATT | 211 | 222 | 150 | 142 | 174 | 158 | 181 | 212 | 190 | 156 | 166 | 222 | 167 | 186 | 211 | 168 | 158 | 181 |
| T>C:CTA | 169 | 162 | 156 | 162 | 150 | 147 | 177 | 173 | 177 | 149 | 178 | 188 | 184 | 170 | 191 | 162 | 158 | 170 |
| T>C:CTC | 855 | 837 | 794 | 982 | 526 | 732 | 876 | 768 | 650 | 616 | 949 | 1098 | 1194 | 956 | 861 | 1193 | 1290 | 1325 |
| T>C:CTG | 197 | 212 | 213 | 176 | 171 | 165 | 206 | 204 | 183 | 151 | 213 | 218 | 208 | 202 | 196 | 205 | 186 | 175 |
| T>C:CTT | 322 | 384 | 342 | 397 | 293 | 346 | 406 | 349 | 344 | 304 | 398 | 450 | 406 | 405 | 385 | 485 | 431 | 513 |
| T>C:GTA | 117 | 137 | 130 | 99 | 112 | 117 | 125 | 119 | 128 | 109 | 117 | 138 | 98 | 123 | 135 | 113 | 111 | 114 |
| T>C:GTC | 133 | 134 | 120 | 127 | 110 | 122 | 129 | 148 | 137 | 117 | 121 | 138 | 130 | 138 | 145 | 127 | 133 | 148 |
| T>C:GTG | 161 | 153 | 139 | 158 | 140 | 146 | 138 | 155 | 163 | 124 | 144 | 173 | 143 | 161 | 177 | 142 | 146 | 136 |
| T>C:GTT | 146 | 162 | 130 | 125 | 129 | 144 | 153 | 162 | 157 | 129 | 130 | 153 | 117 | 145 | 168 | 145 | 129 | 133 |
| T>C:TTA | 122 | 145 | 105 | 133 | 120 | 130 | 127 | 158 | 148 | 118 | 112 | 156 | 133 | 121 | 144 | 123 | 113 | 131 |
| T>C:TTC | 336 | 407 | 345 | 338 | 292 | 370 | 395 | 361 | 351 | 292 | 383 | 433 | 396 | 367 | 437 | 512 | 469 | 679 |
| T>C:TTG | 115 | 128 | 108 | 110 | 109 | 110 | 118 | 137 | 133 | 114 | 111 | 132 | 112 | 100 | 128 | 104 | 122 | 114 |
| T>C:TTT | 301 | 287 | 250 | 287 | 220 | 272 | 279 | 314 | 272 | 251 | 272 | 336 | 256 | 311 | 335 | 290 | 320 | 314 |
| T>G:ATA | 321 | 273 | 299 | 253 | 265 | 289 | 332 | 323 | 331 | 207 | 294 | 323 | 284 | 260 | 364 | 148 | 138 | 161 |
| T>G:ATC | 56 | 57 | 61 | 47 | 60 | 54 | 60 | 54 | 50 | 49 | 40 | 53 | 43 | 54 | 63 | 68 | 53 | 54 |
| T>G:ATG | 99 | 101 | 82 | 102 | 70 | 89 | 98 | 94 | 82 | 78 | 94 | 117 | 102 | 95 | 98 | 142 | 149 | 182 |
| T>G:ATT | 84 | 72 | 61 | 59 | 74 | 78 | 85 | 75 | 82 | 60 | 60 | 94 | 59 | 63 | 95 | 71 | 54 | 87 |
| T>G:CTA | 25 | 33 | 16 | 21 | 25 | 24 | 31 | 33 | 25 | 25 | 23 | 40 | 27 | 29 | 29 | 31 | 30 | 39 |
| T>G:CTC | 31 | 43 | 39 | 44 | 44 | 39 | 47 | 48 | 35 | 30 | 36 | 47 | 42 | 42 | 41 | 35 | 52 | 46 |
| T>G:CTG | 81 | 76 | 90 | 77 | 76 | 70 | 101 | 78 | 79 | 71 | 89 | 105 | 126 | 92 | 89 | 91 | 75 | 103 |
| T>G:CTT | 59 | 64 | 36 | 60 | 58 | 51 | 57 | 54 | 58 | 49 | 60 | 75 | 48 | 58 | 45 | 61 | 61 | 52 |
| T>G:GTA | 133 | 102 | 97 | 128 | 90 | 103 | 109 | 114 | 125 | 100 | 109 | 121 | 141 | 120 | 120 | 113 | 122 | 121 |
| T>G:GTC | 60 | 61 | 46 | 45 | 54 | 49 | 52 | 67 | 44 | 43 | 52 | 50 | 57 | 62 | 53 | 58 | 51 | 64 |
| T>G:GTG | 897 | 938 | 920 | 1144 | 569 | 751 | 1058 | 876 | 712 | 714 | 1039 | 1191 | 1422 | 1094 | 985 | 2799 | 3043 | 2983 |
| T>G:GTT | 202 | 182 | 140 | 192 | 140 | 134 | 155 | 170 | 170 | 126 | 189 | 176 | 203 | 185 | 157 | 155 | 141 | 157 |
| T>G:TTA | 64 | 66 | 54 | 57 | 49 | 55 | 69 | 71 | 74 | 64 | 71 | 85 | 58 | 75 | 88 | 57 | 51 | 72 |
| T>G:TTC | 90 | 82 | 75 | 63 | 79 | 67 | 77 | 91 | 86 | 59 | 71 | 82 | 85 | 72 | 75 | 85 | 80 | 79 |
| T>G:TTG | 169 | 142 | 138 | 143 | 119 | 122 | 139 | 131 | 130 | 114 | 134 | 156 | 143 | 144 | 151 | 126 | 128 | 120 |
| T>G:TTT | 224 | 253 | 142 | 195 | 210 | 178 | 175 | 240 | 196 | 202 | 209 | 177 | 207 | 196 | 169 | 144 | 140 | 149 |
2 Mutation Signatures Analysis
2.1 Mutation Motif Frequencies
2.1.1 Mutation Frequency Heatmap
This is a heatmap of the mutation frequencies by sample. Columns (samples) and rows (motifs) are clustered by similarity within this dataset.
mm_heatmap_labels <-
sample_info %>%
mutate(label = glue("{sample_id} {Genotype}")) %>%
column_to_rownames("sample_id") %>%
{.[colnames(motif_count_matrix), ]} %>%
pull(label)
motif_count_matrix %>%
magrittr::set_colnames(mm_heatmap_labels) %>%
gplots::heatmap.2(
xlab = "Samples",
ylab = "Mutation Motifs", labRow = FALSE,
margins = c(8, 2))
Samples do not appear to cluster by genotype, but sperm samples do cluster separately from liver samples.
2.1.2 Frequencies by Genotype
These are average motif frequencies in samples with each genotype.
plotMutationSpectrum(vr, group = "Genotype") %>% ggplotly()2.2 Estimating the number of mutational processes and their signatures
signatures <- #callr::r(
# func = function(motif_count_matrix){
# library(signeR)
signeR(M = t(motif_count_matrix))## Evaluating models with the number of signatures ranging from 1 to 17, please be patient.
## Evaluating 1 signatures.
## EM algorithm:
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## Running Gibbs sampler for 1 signature...Done.
## Evaluating 5 signatures.
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## Running Gibbs sampler for 5 signatures...Done.
## Evaluating 9 signatures.
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## Running Gibbs sampler for 9 signatures...Done.
## Evaluating 13 signatures.
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## Running Gibbs sampler for 13 signatures...Done.
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## Evaluating 3 signatures (4 evaluations left).
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## Running Gibbs sampler for 2 signatures...Done.
## Evaluating 4 signatures (last evaluation).
## EM algorithm:
##
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## Running Gibbs sampler for 4 signatures...Done.
## The optimal number of signatures is 3.
## Running Gibbs sampler for 3 signatures...Done. # },
# args = list(motif_count_matrix = motif_count_matrix))2.2.1 BIC Plots
Whenever signeR is left to decide which number of signatures is optimal, it will search for the rank Nsig that maximizes the median Bayesian Information Criterion (BIC).
BICboxplot(signatures)
2.2.2 Convergence of signature and exposure matrices
The following plots the MCMC sampled paths for each entry of the signature matrix (P) and their exposures (E). Only post-burnin paths are available for plotting. Those plots are useful for checking if entries have leveled off, reflecting the sampler convergence.
Paths(signatures$SignExposures)
Each row shows the entries and exposures of one signature along sampler iterations.
2.2.3 Signature barplot
SignPlot(signatures$SignExposures)
Signatures barplot with error bars reflecting the sample percentiles 0.05, 0.25, 0.75, and 0.95 for each entry.
2.2.4 Signature heatmap
SignHeat(signatures$SignExposures)
2.2.5 Exposure boxplot
ExposureBoxplot(signatures$SignExposures)
2.2.6 Exposure barplot
ExposureBarplot(signatures$SignExposures)
2.2.7 Exposure heatmap
ExposureHeat(signatures$SignExposures)
2.3 Differential Exposure Analysis
Differential expression analysis was performed on all samples after splitting it to two groups Fhit- and Fhit+ (Tsg or Fhit +/+)
diffexp_labels <-
sample_info %>%
mutate(label = case_when(
str_detect(Genotype, "Tsg") ~ "Fhit+ (Tsg or Fhit +/+)",
str_detect(Genotype, "Fhit +/+") ~ "Fhit+ (Tsg or Fhit +/+)",
TRUE ~ "Fhit-"
)) %>%
column_to_rownames("sample_id") %>%
{.[colnames(motif_count_matrix), ]} %>%
pull(label)
fs::dir_create(here::here("temp"))
pdf(here::here("temp","DiffExpPlot%03d.pdf"), onefile = FALSE)
diff_exposure <- DiffExp(signatures$SignExposures, labels=diffexp_labels)
dev.off()## png
## 2knitr::include_graphics(here::here("temp","DiffExpPlot001.pdf"))Differential expression of signatures. Significant signatures are labeled with the group with the highest contribution from that signature. Here no signatures were significant.
3 Session Info
Git Status:
working directory clean
Git Commit SHA: 2e4edd8371172e9aa902c50fc91e0a5e92951420
Time Stamp: 2021-February-24 17:36:40
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04 LTS
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] cowplot_1.1.0 plotly_4.9.2.1
## [3] SomaticSignatures_2.26.0 signeR_1.16.0
## [5] NMF_0.23.0 cluster_2.1.0
## [7] rngtools_1.5 pkgmaker_0.32.2
## [9] registry_0.5-1 VariantAnnotation_1.36.0
## [11] Rsamtools_2.6.0 Biostrings_2.58.0
## [13] XVector_0.30.0 SummarizedExperiment_1.20.0
## [15] Biobase_2.50.0 GenomicRanges_1.42.0
## [17] GenomeInfoDb_1.26.1 IRanges_2.24.0
## [19] S4Vectors_0.28.0 MatrixGenerics_1.2.0
## [21] matrixStats_0.57.0 BiocGenerics_0.36.0
## [23] glue_1.4.2 forcats_0.5.0
## [25] stringr_1.4.0 dplyr_1.0.2
## [27] purrr_0.3.4 readr_1.4.0
## [29] tidyr_1.1.2 tibble_3.0.4
## [31] ggplot2_3.3.2 tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.1.0 RSQLite_2.2.1 AnnotationDbi_1.52.0
## [4] htmlwidgets_1.5.2 grid_4.0.3 BiocParallel_1.24.1
## [7] munsell_0.5.0 codetools_0.2-16 withr_2.3.0
## [10] colorspace_2.0-0 OrganismDbi_1.32.0 highr_0.8
## [13] knitr_1.30 rstudioapi_0.13 labeling_0.4.2
## [16] git2r_0.27.1 GenomeInfoDbData_1.2.4 bit64_4.0.5
## [19] farver_2.0.3 rprojroot_2.0.2 vctrs_0.3.5
## [22] generics_0.1.0 xfun_0.19 biovizBase_1.38.0
## [25] BiocFileCache_1.14.0 R6_2.5.0 doParallel_1.0.16
## [28] AnnotationFilter_1.14.0 bitops_1.0-6 reshape_0.8.8
## [31] DelayedArray_0.16.0 assertthat_0.2.1 scales_1.1.1
## [34] nnet_7.3-14 gtable_0.3.0 ggbio_1.38.0
## [37] ensembldb_2.14.0 rlang_0.4.9 splines_4.0.3
## [40] rtracklayer_1.50.0 lazyeval_0.2.2 dichromat_2.0-0
## [43] broom_0.7.2 checkmate_2.0.0 BiocManager_1.30.10
## [46] yaml_2.2.1 reshape2_1.4.4 modelr_0.1.8
## [49] GenomicFeatures_1.42.1 crosstalk_1.1.0.1 backports_1.2.0
## [52] Hmisc_4.4-2 RBGL_1.66.0 tools_4.0.3
## [55] gridBase_0.4-7 ellipsis_0.3.1 gplots_3.1.1
## [58] RColorBrewer_1.1-2 proxy_0.4-24 Rcpp_1.0.5
## [61] plyr_1.8.6 base64enc_0.1-3 progress_1.2.2
## [64] zlibbioc_1.36.0 RCurl_1.98-1.2 prettyunits_1.1.1
## [67] rpart_4.1-15 openssl_1.4.3 haven_2.3.1
## [70] fs_1.5.0 here_1.0.0 magrittr_2.0.1
## [73] data.table_1.13.2 reprex_0.3.0 pcaMethods_1.82.0
## [76] ProtGenerics_1.22.0 hms_0.5.3 evaluate_0.14
## [79] xtable_1.8-4 XML_3.99-0.5 jpeg_0.1-8.1
## [82] readxl_1.3.1 gridExtra_2.3 compiler_4.0.3
## [85] biomaRt_2.46.0 KernSmooth_2.23-17 crayon_1.3.4
## [88] htmltools_0.5.0 Formula_1.2-4 lubridate_1.7.9.2
## [91] DBI_1.1.0 dbplyr_2.0.0 rappdirs_0.3.1
## [94] Matrix_1.2-18 cli_2.2.0 pkgconfig_2.0.3
## [97] GenomicAlignments_1.26.0 foreign_0.8-80 xml2_1.3.2
## [100] foreach_1.5.1 PMCMR_4.3 rvest_0.3.6
## [103] digest_0.6.27 graph_1.68.0 rmarkdown_2.5
## [106] cellranger_1.1.0 htmlTable_2.1.0 curl_4.3
## [109] gtools_3.8.2 nloptr_1.2.2.2 lifecycle_0.2.0
## [112] jsonlite_1.7.1 viridisLite_0.3.0 askpass_1.1
## [115] BSgenome_1.58.0 fansi_0.4.1 pillar_1.4.7
## [118] lattice_0.20-41 GGally_2.0.0 httr_1.4.2
## [121] survival_3.2-7 png_0.1-7 iterators_1.0.13
## [124] bit_4.0.4 class_7.3-17 stringi_1.5.3
## [127] blob_1.2.1 latticeExtra_0.6-29 caTools_1.18.0
## [130] memoise_1.1.0